Data Stream Management Systems (DSMS) were developed to be at the heart of every monitoring application (from environmental monitoring, to patient care and outbreaks of diseases, to financial market and cosmic phenomena monitoring). DSMSs are designed to efficiently handle huge volumes of data and large numbers of Continuous Queries (CQs), i.e., exhibit scalability. The need for scalability means that the underlying processing techniques a DSMS adopts should be optimized for high throughput (i.e., tuple output rate). Towards this, two main approaches were proposed in the literature: (1) Multiple Query Optimization (MQO) and (2) Scheduling. While the former aims to statically generate query execution plans which minimize the tuple processing delay, the latter aims to dynamically decide which operator, in a query execution plan, to execute next in order to minimize queuing delays. In this research we focus on optimizing the processing of multiple aggregate continuous queries (ACQs). Specifically, this dissertation will develop optimization techniques for scalable processing of multiple ACQs, taking into consideration the different uncorrelated factors of the processing cost, such as the input rate and ACQs' specifications. It will also consider the general case when some of the ACQs are sub-queries of more complex CQs, which might affect the possibilities for sharing computation. Further, this dissertation will study the synergy between the query optimizer and the query scheduler of the DSMS to develop an aggregation-aware scheduler. The properties of the proposed techniques will be studied analytically whereas their performance advantages will be experimentally evaluated by comparing them with the state-of-the-art, using simulation.